{"id":{"repo_id":"wvu","oai_identifier":"oai:researchrepository.wvu.edu:etd-1975"},"canonical_url":"https://search.dev.ndltd.org/etd/wvu/oai:researchrepository.wvu.edu:etd-1975","repository":{"repo_id":"wvu","name":"West Virginia University","base_url":"https://researchrepository.wvu.edu/do/oai/"},"display":{"title":"Forecasting of work in process quality using Holt-Winters method for missing observations","abstract":"Since the pace of technological change is so great, and since new products and processes may be key to a company's future plans, an increasing number of companies are emphasizing regular and complete technological forecasts affecting their industry. Those companies which have gone far in developing planning premises from their technological forecasts have tended to be high-technology enterprises. What has been done in these instances is to encourage members of their technical staffs to be alert to future developments; to think in terms of the impact of current scientific developments on the future state of technology; and to develop orderly forecasts of how these developments affect the company's products, processes or markets. Many attempts have been made to accurately forecast future and some of the accurate and meaningful methods used to forecast the state of technology are the Delphi technique, the opportunity and goal oriented techniques.;The objective of this research was to develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting was done for at least one period ahead and at the most twelve periods ahead. The developed models were found to give acceptable results with as many as 40% of the total observations missing and this was validated by performing tracking signal analysis.","abstract_html":"Since the pace of technological change is so great, and since new products and processes may be key to a company's future plans, an increasing number of companies are emphasizing regular and complete technological forecasts affecting their industry. Those companies which have gone far in developing planning premises from their technological forecasts have tended to be high-technology enterprises. What has been done in these instances is to encourage members of their technical staffs to be alert to future developments; to think in terms of the impact of current scientific developments on the future state of technology; and to develop orderly forecasts of how these developments affect the company's products, processes or markets. Many attempts have been made to accurately forecast future and some of the accurate and meaningful methods used to forecast the state of technology are the Delphi technique, the opportunity and goal oriented techniques.;The objective of this research was to develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting was done for at least one period ahead and at the most twelve periods ahead. The developed models were found to give acceptable results with as many as 40% of the total observations missing and this was validated by performing tracking signal analysis.","abstract_has_math":false,"creators":["Kayande, Sarang Ram"],"institution":null,"degree_name":"MS","degree_level":"Thesis","degree_discipline":"Industrial and Managements Systems Engineering","degree_department":null,"school":null,"contributors":["Majid Jaraiedi."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1999,"date_issued":"1999-08-01T07:00:00Z","date_published":"1999-08-01T07:00:00Z","updated_at":"2026-07-24T06:15:08Z","subjects":["Industrial engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://researchrepository.wvu.edu/etd/972"],"render_values":[{"text":"https://researchrepository.wvu.edu/etd/972","href":"https://researchrepository.wvu.edu/etd/972","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.33915/etd.972","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Majid Jaraiedi."]},{"key":"dc:creator","label":"Author","values":["Kayande, Sarang Ram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Managements Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Industrial engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.33915/etd.972","https://researchrepository.wvu.edu/etd/972"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Since the pace of technological change is so great, and since new products and processes may be key to a company's future plans, an increasing number of companies are emphasizing regular and complete technological forecasts affecting their industry. Those companies which have gone far in developing planning premises from their technological forecasts have tended to be high-technology enterprises. What has been done in these instances is to encourage members of their technical staffs to be alert to future developments; to think in terms of the impact of current scientific developments on the future state of technology; and to develop orderly forecasts of how these developments affect the company's products, processes or markets. Many attempts have been made to accurately forecast future and some of the accurate and meaningful methods used to forecast the state of technology are the Delphi technique, the opportunity and goal oriented techniques.;The objective of this research was to develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting was done for at least one period ahead and at the most twelve periods ahead. The developed models were found to give acceptable results with as many as 40% of the total observations missing and this was validated by performing tracking signal analysis."]},{"key":"dc:title","label":"Title","values":["Forecasting of work in process quality using Holt-Winters method for missing observations"]}]}],"canonical_facts":{"dc:contributor":["Majid Jaraiedi."],"dc:creator":["Kayande, Sarang Ram"],"dc:date.available":["2019-01-17T08:00:00Z"],"dc:description.abstract":["Since the pace of technological change is so great, and since new products and processes may be key to a company's future plans, an increasing number of companies are emphasizing regular and complete technological forecasts affecting their industry. Those companies which have gone far in developing planning premises from their technological forecasts have tended to be high-technology enterprises. What has been done in these instances is to encourage members of their technical staffs to be alert to future developments; to think in terms of the impact of current scientific developments on the future state of technology; and to develop orderly forecasts of how these developments affect the company's products, processes or markets. Many attempts have been made to accurately forecast future and some of the accurate and meaningful methods used to forecast the state of technology are the Delphi technique, the opportunity and goal oriented techniques.;The objective of this research was to develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting was done for at least one period ahead and at the most twelve periods ahead. The developed models were found to give acceptable results with as many as 40% of the total observations missing and this was validated by performing tracking signal analysis."],"dc:identifier":["https://doi.org/10.33915/etd.972","https://researchrepository.wvu.edu/etd/972"],"dc:subject":["Industrial engineering"],"dc:title":["Forecasting of work in process quality using Holt-Winters method for missing observations"],"thesis:degree_discipline":["Industrial and Managements Systems Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T06:15:08Z"}